Challenge
A leading pan-India retail chain with 400+ stores across 25 cities struggled with inventory mismanagement. Store managers relied on historical sales averages and intuition to place replenishment orders, leading to chronic overstocking of slow-moving items and stockouts of high-demand products. The consequences were severe: capital locked in excess inventory, frequent discounting to clear aged stock, and lost sales from popular items being unavailable.
Compounding the problem, demand signals were fragmented across multiple systems: POS transactions in the ERP, online orders in the e-commerce platform, seasonal event calendars in marketing spreadsheets, and weather data in external APIs. No unified view existed to correlate these factors and produce actionable forecasts at the store-SKU level.
Approach
Factorial AI designed and deployed a unified demand forecasting platform over six months, working closely with the client's operations, IT, and merchandising teams. The solution architecture involved four integrated layers:
- Data unification: We built automated pipelines to ingest POS transactions, online orders, marketing calendars, weather data, and local event schedules into a central data warehouse. Data quality checks, deduplication, and master data management ensured consistent product, store, and time dimensions.
- Feature engineering: We constructed 200+ features per store-SKU combination, including lagged sales, rolling averages, price elasticity, weather correlations, day-of-week patterns, and promotional lift coefficients. Feature importance analysis identified the 40 most predictive signals.
- Model ensemble: We trained an ensemble of gradient-boosted trees, recurrent neural networks, and statistical time-series models, with a meta-learner selecting the best prediction for each product category. The system retrained weekly on fresh data.
- Decision interface: Store managers and merchandisers received a daily dashboard with recommended order quantities, confidence intervals, and explanations for unusual predictions (e.g., "rain forecast increases umbrella demand by 45%").
Technical Architecture
The platform was built on a cloud-native data warehouse with automated ETL pipelines orchestrated via a modern data orchestration framework. Feature stores cached pre-computed features for low-latency inference. The model serving layer exposed REST APIs to the decision dashboard and downstream replenishment systems. All components were monitored with automated alerts for data drift, model degradation, and pipeline failures.
Security was designed for a retail environment with strict PCI-DSS adjacent requirements: end-to-end encryption, role-based access control, and audit logging for all data access and model predictions.
Results
Beyond the headline metrics, the client reported improved relationships with suppliers due to more predictable order patterns, reduced warehouse congestion, and increased confidence among store managers in automated replenishment recommendations. The system is now being extended to seasonal assortment planning and supplier negotiation support.
Client Quote
"Factorial AI did not just build a forecasting model. They built a decision system that our operations teams actually trust and use daily. The inventory reduction alone paid for the project within the first quarter of production."
— VP of Supply Chain, Pan-India Retail Chain
Services used: Data Engineering, Machine Learning, Analytics & Intelligence, Cloud Modernization
Techniques: Time-series forecasting, ensemble modeling, feature engineering, ETL pipeline design, cloud data warehousing
Timeline: 6 months from discovery to production deployment